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Updated: Feb 13, 2026
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神经网络作为热系统:在数字病理学中的应用
bioRxiv : the preprint server for biology
|February 12, 2026
概括
这项研究引入了一个新的框架,以了解深度学习模型如何在数字病理学中学习. 我们发现神经网络在训练过程中会发展出可预测的内部结构,从而提高了对病理学AI的解释性和信任度.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 机器学习可以解释机器学习的解释性.
背景情况:
- 数字病理学的深度学习模型通常被认为是"黑子",由于缺乏可解释性,阻碍了临床采用.
- 了解神经网络的内部学习动态对于建立信任和确保可靠的临床应用至关重要.
研究的目的:
- 开发和应用一个框架,以实证地描述数字病理学中神经网络的培训时间学习动态.
- 在模型优化过程中调查激活结构,重量轨迹和光谱组织的演变.
主要方法:
- 使用TCGA BRCA全幻灯片图像与甲基化代理作为回归目标.
- 训练了一个视觉转换器模型,并仔细跟踪了它在20个时代的时代内行为的行为.
- 测量了激活结构,重量演变和光谱组织,以分析学习动态.
主要成果:
- 在神经网络训练过程中观察到可重复的结构签名,包括稳定的激活模块的形成,模块性增加和表示率下降 (高达60%).
- 重量轨迹表明有界的扩散,汇聚到一个稳定的状态,表明一个缓和的随机过程.
- 模型的注意力从结构区域转移到核区域,与复制性压力的组织学指标相关.
结论:
- 神经网络在训练过程中表现出可预测和可量化的内部结构发展,为解释提供了一种机械镜头.
- 该框架提供了可视化和测量学习动态的实用方法,提高了病理学AI模型的可解释性.
- 通过,模块化和随机稳定来描述学习动态,可以消除人工智能模型如何获得生物相关表示的神秘性.
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